In mobile communication networks, there is always a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the mobile communication network is deployed.
One component in a mobile communication network is the network node. In general terms, a network node facilitates wireless communication between a wireless electronic device and a network. In order to do so the network node comprises hardware as well as software. A transceiver unit is responsible for transmission and reception of radio signals. The signals to be transmitted from the transceiver unit are provided to a radio frequency power amplifier (RFPA) which amplifies the signals from the transceiver unit before being fed to an antenna. The RFPA may be integrated with the transceiver unit. RFPA are highly nonlinear elements which commonly inflicts distortion on to the signal when driven in compression. In order to compensate for this, a pre-distorter may be used in order to cancel out the distortion added by the RFPA. For this purpose it is common to use a black-box model of the RFPA to model and invert the distortion components.
Known models for digital pre-distortion are very often based on different variants of the Volterra-series, or memory polynomials. On example of the first approach is by Zhu, A. et al; “Dynamic Deviation Reduction-Based Volterra Behavioral Modeling of RF Power Amplifiers”, IEEE Transactions on Microwave Theory and Techniques, 2006. One example of the second approach is by Morgan, D. R et al; “A generalized memory polynomial model for digital predistortion of RF power amplifiers”, IEEE Transactions on Signal Processing.
Since most of the known models are linear in parameters, they may be described in a matrix form asy=Hθ+w, where H is the N-by-M basis regression matrix which consists of permutations on the input x, θ is the M-by-1 model coefficient vector, y is the N-by-1 vector containing the output samples from the RFPA and w is the N-by-1 observation noise vector. For simplicity, the observation noise is commonly assumed to be additive and Gaussian. In the noise-less case, one step of the Gauss-Newton iteration yields the least squares error optimal coefficient vector θLS asθLS=(HTH)−1HTy, where y is a vector containing the observations made at the RFPA output. From the equation immediately above, the Moore-Penrose pseudo inverse is identified asH+=(HTH)−1HT.
Since noiseless observations are impossible in practice, the Gauss-Newton-step (GNS) may require several iterations in order to converge, which is done iteratively asθ(n+1)=θ(n)+μH+(x−y),where α<1 is the step-size and θ(n) is the coefficient vector at the n:th iteration.
However, there is still a need for an improved determination of the non-linearity of RFPAs in network nodes.